This document is the summary of the Introduction to R workshop.
All correspondence related to this document should be addressed to:
Omid Ghasemi (Macquarie University, Sydney, NSW, 2109, AUSTRALIA)
Email: omidreza.ghasemi@hdr.mq.edu.auThe aim of the study is to test if simple arguments are more effective in belief revision than more complex arguments. To that end, we present participants with an imaginary scenario (two alien creatures on a planet) and a theory (one creature is predator and the other one is prey) and we ask them to rate the likelihood truth of the theory based on a simple fact (We adapted this method from Gregg et al.,2017; see the original study here). Then, in a between-subject manipulation, participants will be presented with either 6 simple arguments (Modus Ponens conditionals) or 6 more complex arguments (Modus Tollens conditionals), and they will be asked to rate the likelihood truth of the initial theory on 7 stages.
The first stage is the base rating stage. The next three stages include supportive arguments of the theory and the last three arguments include disproving arguments of the theory. We hypothesized that the group with simple arguments shows better persuasion (as it reflects in higher ratings for the supportive arguments) and better dissuasion (as it reflects in lower ratings for the opposing arguments).
In the last part of the study, participants will be asked to answer several cognitive capacity/style measures including thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales. We hypothesized that cognitive ability, cognitive style, and open-mindedness are positive predictors of persuasion and dissuasion. These associations should be more pronounced for participants in the group with complex arguments because the ability and willingness to engage in deliberative thinking may favor participants to assess the underlying logical structure of those arguments. However, for participants in the simple group, the logical structure of arguments is more evident, so participants with lower ability can still assess the logical status of those arguments.
Thus, our hypotheses for this experiment are as follows:
Participants in the group with simple arguments have higher ratings for supportive arguments (They are more easily persuaded than those in the group with complex arguments).
Participants in the group with simple arguments have lower ratings for opposing arguments (They are more easily dissuaded than those in the group with complex arguments).
There are significant associations between thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales with both persuasion and dissuasion indexes in each group and in the entire sample. The relationship between these measures should be stronger, although not significantly, for participants in the group with complex arguments.
First, we need to design the experiment. For this experiment, we use online platforms for data collection. There are several options such as Gorilla, JSpsych, Qualtrics, psychoJS (pavlovia), etc. Since we do not need any reaction time data, we simply use Qualtrics. For an overview of different lab-based and online platforms, see here.
Next, we need to decide on the number of participants (sample size). For this study, we do not sue power analysis since we cannot access more than 120 participants. However, it is highly suggested calculate sample size using power estimation. You can find some nice tutorials on how to do that here, here, and here.
After we created the experiment and decided on the sample size, the next step is to preresigter the study. However, it would be better to do a pilot with 4 or 5 participants, clean all the data, do the desired analysis, and then pre-register the analysis and those codes. You can find the preregistration form for the current study here.
Finally, we need to restructure our project in a tidy folder with different sub-folders. Having a clean and tidy folder structure can save us! There are different formats of folder structure (for example, see here and here), but for now, we use the following structure:
# load libraries
library(tidyverse)
library(here)
library(janitor)
library(broom)
library(afex)
library(emmeans)
library(knitr)
library(kableExtra)
library(ggsci)
library(patchwork)
library(skimr)
# install.packages("devtools")
# devtools::install_github("easystats/correlation")
library("correlation")
options(scipen=999) # turn off scientific notations
options(contrasts = c('contr.sum','contr.poly')) # set the contrast sum globally
options(knitr.kable.NA = '')
Artwork by Allison Horst: https://github.com/allisonhorst/stats-illustrations
R can be used as a calculator. For mathematical purposes, be careful of the order in which R executes the commands.
10 + 10
## [1] 20
4 ^ 2
## [1] 16
(250 / 500) * 100
## [1] 50
R is a bit flexible with spacing (but no spacing in the name of variables and words)
10+10
## [1] 20
10 + 10
## [1] 20
R can sometimes tell that you’re not finished yet
10 +
How to create a variable? Variable assignment using <- and =. Note that R is case sensitive for everything
pay <- 250
month = 12
pay * month
## [1] 3000
salary <- pay * month
Few points in naming variables and vectors: use short, informative words, keep same method (e.g., not using capital words, use only _ or . ).
Function is a set of statements combined together to perform a specific task. When we use a block of code repeatedly, we can convert it to a function. To write a function, first, you need to define it:
my_multiplier <- function(a,b){
result = a * b
return (result)
}
This code do nothing. To get a result, you need to call it:
my_multiplier (2,4)
## [1] 8
Fortunately, you do not need to write everything from scratch. R has lots of built-in functions that you can use:
round(54.6787)
## [1] 55
round(54.5787, digits = 2)
## [1] 54.58
Use ? before the function name to get some help. For example, ?round. You will see many functions in the rest of the workshop.
function class() is used to show what is the type of a variable.
TRUE, FALSE can be abbreviated as T, F. They has to be capital, ‘true’ is not a logical data:class(TRUE)
## [1] "logical"
class(F)
## [1] "logical"
class(2)
## [1] "numeric"
class(13.46)
## [1] "numeric"
class("ha ha ha ha")
## [1] "character"
class("56.6")
## [1] "character"
class("TRUE")
## [1] "character"
Can we change the type of data in a variable? Yes, you need to use the function as.---()
as.numeric(TRUE)
## [1] 1
as.character(4)
## [1] "4"
as.numeric("4.5")
## [1] 4.5
as.numeric("Hello")
## Warning: NAs introduced by coercion
## [1] NA
Vector: when there are more than one number or letter stored. Use the combine function c() for that.
sale <- c(1, 2, 3,4, 5, 6, 7, 8, 9, 10) # also sale <- c(1:10)
sale <- c(1:10)
sale * sale
## [1] 1 4 9 16 25 36 49 64 81 100
Subsetting a vector:
days <- c("Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
days[2]
## [1] "Sunday"
days[-2]
## [1] "Saturday" "Monday" "Tuesday" "Wednesday" "Thursday" "Friday"
days[c(2, 3, 4)]
## [1] "Sunday" "Monday" "Tuesday"
Create a vector named my_vector with numbers from 0 to 1000 in it:
my_vector <- (0:1000)
mean(my_vector)
## [1] 500
median(my_vector)
## [1] 500
min(my_vector)
## [1] 0
range(my_vector)
## [1] 0 1000
class(my_vector)
## [1] "integer"
sum(my_vector)
## [1] 500500
sd(my_vector)
## [1] 289.1081
List: allows you to gather a variety of objects under one name (that is, the name of the list) in an ordered way. These objects can be matrices, vectors, data frames, even other list.
my_list = list(sale, 1, 3, 4:7, "HELLO", "hello", FALSE)
my_list
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[2]]
## [1] 1
##
## [[3]]
## [1] 3
##
## [[4]]
## [1] 4 5 6 7
##
## [[5]]
## [1] "HELLO"
##
## [[6]]
## [1] "hello"
##
## [[7]]
## [1] FALSE
Factor: Factors store the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character. For example, variable gender with “male” and “female” entries:
gender <- c("male", "male", "male", " female", "female", "female")
gender <- factor(gender)
R now treats gender as a nominal (categorical) variable: 1=female, 2=male internally (alphabetically).
summary(gender)
## female female male
## 1 2 3
Question: why when we ran the above function i.e. summary(), it showed three and not two levels of the data? Hint: run ‘gender’.
gender
## [1] male male male female female female
## Levels: female female male
So, be careful of spaces!
Create a gender factor with 30 male and 40 females (Hint: use the rep() function):
gender <- c(rep("male",30), rep("female", 40))
gender <- factor(gender)
gender
## [1] male male male male male male male male male male
## [11] male male male male male male male male male male
## [21] male male male male male male male male male male
## [31] female female female female female female female female female female
## [41] female female female female female female female female female female
## [51] female female female female female female female female female female
## [61] female female female female female female female female female female
## Levels: female male
There are two types of categorical variables: nominal and ordinal. How to create ordered factors (when the variable is nominal and values can be ordered)? We should add two additional arguments to the factor() function: ordered = TRUE, and levels = c("level1", "level2"). For example, we have a vector that shows participants’ education level.
edu<-c(3,2,3,4,1,2,2,3,4)
education<-factor(edu, ordered = TRUE)
levels(education) <- c("Primary school","high school","College","Uni graduated")
education
## [1] College high school College Uni graduated
## [5] Primary school high school high school College
## [9] Uni graduated
## Levels: Primary school < high school < College < Uni graduated
We have a factor with patient and control values. Here, the first level is control and the second level is patient. Change the order of levels, so patient would be the first level:
health_status <- factor(c(rep('patient',5),rep('control',5)))
health_status
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: control patient
health_status_reordered <- factor(health_status, levels = c('patient','control'))
health_status_reordered
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: patient control
Finally, can you relabel both levels to uppercase characters? (Hint: check ?factor)
health_status_relabeled <- factor(health_status, levels = c('patient','control'), labels = c('Patient','Control'))
health_status_relabeled
## [1] Patient Patient Patient Patient Patient Control Control Control
## [9] Control Control
## Levels: Patient Control
Matrices: All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. It can be created using a vector input to the matrix function.
my_matrix = matrix(c(1,2,3,4,5,6,7,8,9), nrow = 3, ncol = 3)
my_matrix
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
Data frames: (two-dimensional objects) can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type. Let’s create a dataframe:
id <- 1:200
group <- c(rep("Psychotherapy", 100), rep("Medication", 100))
response <- c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5))
my_dataframe <-data.frame(Patient = id,
Treatment = group,
Response = response)
We also could have done the below
my_dataframe <-data.frame(Patient = c(1:200),
Treatment = c(rep("Psychotherapy", 100), rep("Medication", 100)),
Response = c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5)))
In large data sets, the function head() enables you to show the first observations of a data frames. Similarly, the function tail() prints out the last observations in your data set.
head(my_dataframe)
tail(my_dataframe)
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 27.18813 |
| 2 | Psychotherapy | 30.77725 |
| 3 | Psychotherapy | 23.28473 |
| 4 | Psychotherapy | 31.29284 |
| 5 | Psychotherapy | 34.30820 |
| 6 | Psychotherapy | 27.13006 |
| Patient | Treatment | Response | |
|---|---|---|---|
| 195 | 195 | Medication | 24.94144 |
| 196 | 196 | Medication | 29.23121 |
| 197 | 197 | Medication | 18.28316 |
| 198 | 198 | Medication | 29.72068 |
| 199 | 199 | Medication | 20.40847 |
| 200 | 200 | Medication | 25.52196 |
Similar to vectors and matrices, brackets [] are used to selects data from rows and columns in data.frames:
my_dataframe[35, 3]
## [1] 26.66354
How can we get all columns, but only for the first 10 participants?
my_dataframe[1:10, ]
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 27.18813 |
| 2 | Psychotherapy | 30.77725 |
| 3 | Psychotherapy | 23.28473 |
| 4 | Psychotherapy | 31.29284 |
| 5 | Psychotherapy | 34.30820 |
| 6 | Psychotherapy | 27.13006 |
| 7 | Psychotherapy | 29.75732 |
| 8 | Psychotherapy | 24.90917 |
| 9 | Psychotherapy | 27.64901 |
| 10 | Psychotherapy | 25.00871 |
How to get only the Response column for all participants?
my_dataframe[ , 3]
## [1] 27.188126 30.777253 23.284726 31.292836 34.308199 27.130061 29.757324
## [8] 24.909171 27.649012 25.008706 29.404166 23.268888 32.642239 25.665492
## [15] 32.688967 23.728734 33.438426 29.227611 25.100626 27.441264 32.821064
## [22] 29.089883 24.758246 28.054203 26.842840 35.034423 38.382492 22.512905
## [29] 27.165009 30.901339 28.444171 26.189879 24.366393 22.861877 26.663542
## [36] 32.881681 25.600396 32.844674 25.332858 36.813089 26.939681 33.194629
## [43] 24.394595 28.249130 34.452274 32.760293 31.838643 28.933429 28.123214
## [50] 33.227934 26.928414 25.758564 35.985862 24.497804 33.595179 38.379838
## [57] 38.312211 17.521292 34.668226 28.943036 27.116053 23.293048 32.439473
## [64] 25.738447 29.837958 20.592843 26.754394 33.437326 20.045950 23.131255
## [71] 19.809381 36.086198 36.003911 34.430729 32.851844 28.116000 27.403396
## [78] 28.358421 27.721485 36.395176 27.819444 35.244008 19.941209 30.296163
## [85] 29.734636 31.296918 32.184261 19.298451 28.209186 34.043898 34.110418
## [92] 33.966079 29.794997 33.549395 33.550190 35.598820 27.951357 35.407784
## [99] 27.490922 26.300815 18.609819 16.529805 25.528157 39.126264 18.133777
## [106] 16.990457 26.196786 21.368884 26.850982 18.047324 22.599537 21.818001
## [113] 16.749515 22.013183 23.585421 31.190250 30.474315 18.308010 22.334016
## [120] 23.815240 20.729545 16.023402 19.396540 24.865730 27.707688 23.789597
## [127] 32.152586 27.547219 24.882735 23.200173 38.652595 30.623339 24.800595
## [134] 24.364645 30.318903 30.892900 22.652342 28.482825 18.787570 18.797272
## [141] 29.301656 9.405295 30.745603 34.779409 28.458336 18.379039 24.746245
## [148] 26.365894 25.614790 19.497229 22.633527 20.036444 18.892125 28.465954
## [155] 25.940818 28.956663 24.647856 24.121596 30.138271 25.449607 19.229854
## [162] 23.228124 21.151571 27.708903 18.777325 21.279991 28.266972 27.302461
## [169] 17.732701 30.240098 24.446121 22.412089 20.034263 24.806594 21.042250
## [176] 18.654422 22.054471 21.464672 24.615761 35.970194 20.100450 24.561692
## [183] 24.941381 25.967291 30.430921 26.250760 19.291365 30.255276 24.191861
## [190] 32.343982 20.052122 24.993945 32.255221 33.965813 24.941444 29.231208
## [197] 18.283163 29.720682 20.408468 25.521963
Another easier way for selecting particular items is using their names that is more helpful than number of the rows in large data sets:
my_dataframe[ , "Response"]
# OR:
my_dataframe$Response